The present embodiments relate to assessing the road quality of one or more road segments in a road network.
Road networks typically include one or more road segments containing road anomalies, such as, for example, potholes, bumps, railroad crossings, drainage ditches, and manhole covers. These road anomalies are often a source of irritation or frustration for drivers of vehicles traveling on or along these road segments. These road anomalies may also cause damage to vehicles that swerve to avoid or drive over, or even close to, one or more of the anomalies. In turn, drivers may pass their frustration on to and/or seek compensation for damage from the government entity responsible for maintaining and/or repairing the road segments containing the anomalies.
Systems have been developed that help government entities identify road anomalies, but these systems are associated with a number of disadvantages. The City of Chicago has, for example, set up a website that allows drivers to report road anomalies such as potholes and large bumps. Road anomalies are, however, only identified when drivers take note of road anomalies and the location of the road anomalies, decide to report the road anomalies, and actually report the road anomalies at a later time. Since there is a gap between the observation and the reporting of the road anomalies, drivers may not accurately report the precise location of the road anomalies. In some cases, drivers may totally forget to report the road anomalies. The City of Boston has, as another example, developed a mobile application, known as “Street Bump,” that collects motion and location data using the accelerometer and global positioning system (GPS) of the associated mobile device. In turn, Street Bump uploads the collected motion and location data to a central server that attempts to identify potholes and large bumps from the uploaded data. Boston's Street Bump application has generally been regarded as a failure because it has a small user base (the application does not provide any other functionality, so few drivers actually utilize the application), identifies a large number of false positives, such as railroad crossings and manhole covers, that are not actually indicative of road quality, and requires that drivers actually drive into the potholes or over the large bumps for those anomalies to be accurately identified.